Response quality
Correctness, completeness, factuality, relevance, instruction following, style and domain-specific quality.
Evaluate models
Know where your model works and where it fails.
Build evaluation datasets, rubrics and human review workflows for model quality, safety, reasoning, retrieval, tool use and full agent trajectories.
Evaluation consolebenchmark / current release
OverviewDimensionsFailuresRuns
Model snapshotRelease readyreference set connected
model
signal
signal
Rubric-drivenMeasure what matters
Trace-awareInspect the path
Human-calibratedResolve ambiguity
Built around the real work
Capabilities that move a model from promising to useful.
Choose the parts of the data and quality workflow that match the behavior, market or modality you are building for.
BUILD
Designed for the next decision, not the last task.
Every capability is shaped around downstream use: what the model needs to learn, what reviewers need to judge and what your team needs to ship with confidence.
Talk through your workflow01
Reasoning & complex tasks
Expert-created tasks, detailed rubrics and reference solutions for technical and analytical work.
Safety & policy behavior
Refusal quality, harmful-content handling, jailbreak resilience, policy adherence and adversarial behavior.
Retrieval & RAG
Document relevance, groundedness, citation support, context use and retrieval failure cases.
Tool use & decisions
Whether the agent selected the right tool, supplied valid arguments and stayed within permitted scope.
Agent trajectories
Evaluate tool paths, unnecessary steps, recovery, loops, policy gates and final task completion.
Release reviewBenchmark connected
DimensionEvidenceReview
Accuracyready
Reasoningready
Safetyready
Retrievalinspect
Agent outcomeready
Every score points to a decision.Slice, rubric and reviewer context stay attached.
Don’t grade an agent like a chatbot
Measure outputs, decisions and the path between them.
For simple tasks, the final answer may be enough. For agents, evaluation must also test tool selection, arguments, retrieval, trajectory and recovery behavior.
- Final response quality and evidence support
- Tool selection, argument validity and policy gates
- Ordered or acceptable alternative trajectories and recovery
The operating model
Turn one-off testing into a regression system.
Keep the benchmark connected to the product behavior it is meant to protect.
Define success
Translate product requirements into measurable dimensions and failure taxonomies.
Build dataset
Curate real, synthetic and adversarial cases with references or expected trajectories.
Calibrate graders
Align humans and automated evaluators on rubrics, examples and edge cases.
Benchmark
Compare models, prompts, retrieval strategies or agent versions on the same set.
Loop failures
Promote important production failures into the benchmark and targeted training data.
Quality that holds up in production
Use the right judge for the question.
No single evaluator works for every task. Combine deterministic checks, model-based graders and calibrated human experts based on ambiguity and risk.
- Deterministic and code-based checks where rules are clear
- LLM-as-judge and reference comparison with calibration
- Human expert scoring, pairwise comparison and adjudication
Model qualityCorrectness, factuality, completeness, reasoning and domain validity
Agent executionTools, arguments, trajectory, recovery, loops and task completion
TrustSafety, policy behavior, groundedness, citations and evidence support
Program outputBenchmark, rubric, failure taxonomy, findings and next actions
What arrives at the end
Deliverables your team can use.
Clear outputs, stable structure and the context needed to move the work into training, evaluation or production.
01Evaluation specification
02Custom benchmark dataset
03Scoring framework and rubrics
04Model or system comparison
05Human evaluation results
06Failure taxonomy and examples
07Expert findings
08Launch-readiness assessment
09Regression evaluation suite
Where it fits
Model releases
Compare versions against the same representative test set.
RAG systems
Separate retrieval relevance, groundedness and answer faithfulness.
AI agents
Score tool decisions, trajectories, recovery and final outcomes.
Safety programs
Create policy edge cases and review behavior under adversarial pressure.
Start with the outcome
Build an eval set your team can run every release.
Start with the behaviors that matter most in production. We’ll help define the benchmark, rubric, expert profile and evaluation workflow around it.